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Multitask Learning Framework For Screening Of Corrosion Inhibitors For Mild Steel

Picture for Multitask Learning Framework For Screening Of Corrosion Inhibitors For Mild Steel
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Corrosion of metallic structures is a ubiquitous problem in industries such as power generation, oil and gas, pulp and paper, metals processing etc. which also results in significant financial losses. According to the National Association of Corrosion Engineers (NACE) International report, the global cost of corrosion was ~ 2.5 trillion USD in 2013 - close to 3.4 percent GDP of the entire world. The use of corrosion inhibitors is one of the most effective and economical ways to mitigate corrosion of metal and alloy components. Corrosion inhibitors are substances that are added in small quantities in corrosive media to protect metal and alloy components from corrosion.

Product Number: 51322-17757-SG
Author: Abhishek Agarwal, Pradeep Rathore, Dharmendr Kumar, Sri Harsha Nistala, S Sanjana, Vinay Jain, Beena Rai
Publication Date: 2022
Industries: Corrosion Monitoring and Control , Coatings and Linings , Coatings
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Corrosion inhibitors are useful to mitigate corrosion of metal/alloy components. However, traditional corrosion inhibitors are toxic and need to be replaced by greener alternatives. Efficient screening models are required to find molecules with desired properties from millions of molecules available in public domain. To make these models, database of experimental inhibition efficiency of molecules is essential. In this work, we have developed a computational framework to accelerate the discovery of new corrosion inhibitors. We have used machine learning based algorithms to predict corrosion inhibition efficiency of organic molecules for steel in hydrochloric acid by using structural information of the molecules along with experimental conditions. Our multitask learning based neural network architecture was able to outperform traditional machine learning algorithms such as random forest, lasso and ridge regression. We have also created the largest dataset for predictive modelling of corrosion inhibitors for steel. Besides, we have also used the model to screen molecules from the ZINC15 dataset and found potential inhibitors with high inhibition efficiency.

Corrosion inhibitors are useful to mitigate corrosion of metal/alloy components. However, traditional corrosion inhibitors are toxic and need to be replaced by greener alternatives. Efficient screening models are required to find molecules with desired properties from millions of molecules available in public domain. To make these models, database of experimental inhibition efficiency of molecules is essential. In this work, we have developed a computational framework to accelerate the discovery of new corrosion inhibitors. We have used machine learning based algorithms to predict corrosion inhibition efficiency of organic molecules for steel in hydrochloric acid by using structural information of the molecules along with experimental conditions. Our multitask learning based neural network architecture was able to outperform traditional machine learning algorithms such as random forest, lasso and ridge regression. We have also created the largest dataset for predictive modelling of corrosion inhibitors for steel. Besides, we have also used the model to screen molecules from the ZINC15 dataset and found potential inhibitors with high inhibition efficiency.

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Picture for New Experiences With Explosion Clad Alloys UNS N06058 And UNS N06059
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New Experiences With Explosion Clad Alloys UNS N06058 And UNS N06059

Product Number: 51322-17650-SG
Author: P. Hübner, H. Alves, O. Sarrat, R. Behrens, D. Niespodziany, J. König
Publication Date: 2022
$20.00

The alloys used as clad material for this study are members of the so-called “C-family”. It consists of Ni-Cr-Mo alloys, which are known for combining the corrosion resistance of Ni-Cr alloys in oxidizing media with corrosion resistance of Ni-Mo alloys in reducing media. As a result, these materials have proven to be extremely durable in a wide range of highly aggressive media. The development of these materials started in the 1930s with Alloy C. This alloy showed remarkable corrosion resistance in a wide spread of media, low sensitivity for pitting or crevice corrosion and virtual immunity to chloride induced stress corrosion cracking.
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Monitoring Localized Corrosion Distribution Of Steel Bar In Concrete With OFDR Distributed Optical Fiber

Product Number: 51322-17777-SG
Author: Fujian Tang, Jialiang Hu, Hong-Nan Li, Liang Ren, Els Verstrynge
Publication Date: 2022
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Steel rebars in concrete structures are usually protected from corrosion by a thin layer of passive film, which is formed due to the high alkalinity of concrete pore solution.1-2 However, this protective passive film could be damaged by penetration of chloride into concrete structures in marine environments or exposure to the use of de-icing salt for the removal of snow and ice in winter times.3 Penetration of chloride would impair the passive film locally and initiate pitting corrosion.
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Naval Aviation Environmental Severity Correlation

Product Number: 51322-17808-SG
Author: Christine Sanders, Raymond Santucci, Jr
Publication Date: 2022
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The exposure environment of an engineering material quite often has a large impact on how that material behaves over time. Environments are distinguished by differences in meteorological patterns, geography, salinity, Ultraviolet (UV) radiation, etc1-3. Thus, the degradation of various materials scales proportionately to the characteristics of the exposure site, with more severe sites leading to worse degradation. Developing an understanding of how the local environment impacts the corrosion rates of metals and the deterioration of anti-corrosion coatings is critical for informing asset maintenance schedules and lifetime predictions4. 
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